Development and Early Formative Evaluation of a Gamification-Based Web Programming LMS with an Adaptive Content Recommendation Mechanism Based on Learning Performance Index

Abstract

The low completion rate of online courses and the absence of localized web programming platforms integrating gamification with adaptive recommendations background this study. This research aims to develop StepByWeb, a gamified web programming Learning Management System (LMS) driven by a proposed Learning Performance Index (LPI). Calculated from weighted cognitive achievement, learning consistency, and completion rates, the LPI automatically maps users into four performance tiers (High, Moderate, Low, Critical). The system then dynamically delivers differentiated content recommendations tailored to each tier’s specific needs, such as advanced challenges for High LPI or foundational review materials for Critical LPI. Developed using the ADDIE model with React and Supabase, the platform underwent functional and usability evaluations. Black Box Testing demonstrated a 95% functional success rate, while User Acceptance Testing via the System Usability Scale (SUS) involving 20 respondents yielded an average score of 81.12 (Grade A, Excellent). These initial formative evaluation findings indicate that StepByWeb is functionally feasible and well-accepted by users, successfully implementing the LPI as a personalized content recommendation mechanism. Longitudinal validation of learning outcomes remains an agenda for future research.